Reducing the impacts of natural hazards through forecast-based action: from early warning to early action

The Sendai Framework for disaster risk reduction (SFDRR) and its seventh global target recognizes that increased efforts are required to develop risk-informed and impact-based multi-hazard early warning systems. Despite significant advances in disaster forecasting and warning technology, it remains challenging to produce useful forecasts and warnings that are understood and used to trigger early actions. Overcoming these challenges requires understanding of the reliability of forecast tools and implementation barriers in combination with the development of new risk-informed processes. It also requires a commitment to create and share risk and impact data and to co-produce impact-based forecasting models and services. To deal with the problem of coming into action in response to imperfect forecasts, novel science-based concepts have recently emerged. As an example, Forecast-based Financing and Impact-based Multi-Hazard Early Warning Systems are currently being implemented operationally by both governmental and non-governmental organisations in several countries as a result of increasing international effort by several organizations such as the WMO, World Bank, IFRC and UNDRR to reduce disaster losses and ensuring reaching the objectives of SFDRR. This session aims to showcase lessons learnt and best practices on impact-based multi-hazards early warning system from the perspective of both the knowledge producers and users. It presents novel methods to translate forecast of various climate-related and geohazards into an impact-based forecast. The session addresses the role of humanitarian agencies, scientists and communities at risk in creating standard operating procedures for economically feasible actions and reflects on the influence of forecast uncertainty across different time scales in decision-making. Moreover, it provides an overview of state-of-the-art methods, such as using Artificial Intelligence, big data and space applications, and presents innovative ways of addressing the difficulties in implementing forecast-based actions. We invite submissions on the development and use of operational impact-based forecast systems for early action; developing cost-efficient portfolios of early actions for climate/geo-related impact preparedness such as cash-transfer for droughts, weather-based insurance for floods; assessments on the types and costs of possible forecast-based disaster risk management actions; practical applications of impact forecasts.

Convener: Marc van den Homberg | Co-conveners: Andrea Ficchì, Gabriela Guimarães Nobre, David MacLeod, Annegien Tijssen
vPICO presentations
| Thu, 29 Apr, 11:00–11:45 (CEST)

vPICO presentations: Thu, 29 Apr

Gabriela Guimarães Nobre, Marthe Wens, and Marc Van den Homberg

The project “Forecast based Financing for Food Security” (F4S) aims to provide a deeper understanding of how key drivers of food insecurity can be forecasted early enough to enable the trigger of humanitarian action in pilot areas in Ethiopia, Kenya, and Uganda. In combination with the knowledge being produced about early warning and forecasting, F4S also wants to inform early action (e.g. ex-ante cash transfers) that can reduce the risk of food insecurity. F4S has been achieving this goal through three main pillars:  (i) modelling, (ii) local knowledge and (iii) cost-benefits analysis.

This PICO presentation shares the lessons learnt and results of the F4S project. Moreover, it  hopes to trigger the discussion on how the scientific community together with local stakeholders and communities can co-produce knowledge that is relevant to local action, focussing on three result areas.

  • (i) The impact-based forecasting model to understand the key drivers of food insecurity in agricultural, agro-pastoral, and pastoral regions. Simple to more complex Machine Learning algorithms have been developed, applied and benchmarked. These algorithms were used to forecast, 6 to 1 months ahead, key indicators of food insecurity such as the shortage of calories and the transitions in IPC classes. Local knowledge was used to inform the selection of the predictors of the Machine Learning algorithm.
  • (ii) The results of a household survey and individual choice experiments among 600 household members of vulnerable communities. The survey collected local knowledge on early warning (food insecurity triggers) and early actions traditionally taken to lessen food insecurity. The novel choice experiment consisted of giving potential beneficiaries of ex-ante cash transfers the choice between different timings and frequencies of cash transfers for different drought and food security scenarios. The results provided a better understanding of people’s willingness to invest in risk reduction actions and individual preferences on key design elements of cash transfer mechanisms.
  • (iii) The evaluation of the cost-effectiveness of different cash transfer mechanisms that investigates how cash transfer programs can achieve a significant reduction in costs if cash is disbursed prior to the food insecurity occurrence.

This knowledge, as produced on the three areas above, is being currently used to improve the design of ex-ante cash programs. In addition to yielding significant cost savings, the project has found that cash transfer programs can be a more dignified solution when disbursed early enough. Cash transfer programs have the potential to increase the range of early action by beneficiaries that ultimately can reduce the risk of food insecurity and possibly malnutrion in vulnerable communities.

How to cite: Guimarães Nobre, G., Wens, M., and Van den Homberg, M.: Forecast based Financing for Food Security : from early warning to early action in Eastern Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13115, https://doi.org/10.5194/egusphere-egu21-13115, 2021.

Amber Emeis, Gabriele Guimarães Nobre, Marc van den Homberg, Aklilu Teklesadik, and Vicky Boult

The Ethiopian agricultural system is predominantly formed by smallholder and rainfed farmers. Their local food systems are greatly reliant on seasonal climate variability. Often, droughts and food insecurity are interlinked and can negatively impact local communities. In addition to climate variability, a number of socio-economic factors such as multiple harvest failures, distance to markets and pre-existing inequalities are well known to impact people’s access to safe, sufficient and affordable food. Anticipatory action to avoid a situation of food security crisis often requires the understanding of how many people can be potentially affected by a shock and how much financing should be invested. 

This study aims to forecast shortages in maize calories, which is defined as the percentage of the population for which not sufficient maize calories are available. Forecast models were developed for agricultural and agro-pastoral livelihood zones in Ethiopia in connection to the unimodal and bimodal rain seasons by using the Fast-and-Frugal Trees Algorithm. To forecast shortage events, five variables were used ranging from socio-economic to physical drivers: 1) soil moisture (Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT)), 2) maize production from the previous season, 3) the Gini index, 4) the main livelihood mode and 5) the travel time to the closest market. The lead time of the model is increased using TAMSAT forecast data to create a wider window for action before harvesting. 

The skill of the model with increased lead-time in relation to the cost of the humanitarian intervention was analysed to examine the cost-effectiveness of forecast-based action. Therefore, the cost of acting early (through a scheme of cash transfer) has been compared to ex-post interventions. To assess the cost-effectiveness of the cash transfer, the prices of a basket of goods before and after harvesting are included in the model with the assumption that prices of staple crops increase when there is scarcity (food insecurity). With these results, the study will explore the practicality of implementing the anticipatory action by looking at the implications of model uncertainty (False Alarms, ‘acting in vain’). Likewise, the possible opportunities and challenges in regards to operationalizing the model will be deliberated. Accordingly, this study hopes to contribute to the use of early warning early action systems by humanitarian agencies in reducing the impacts of natural hazards. 

How to cite: Emeis, A., Guimarães Nobre, G., van den Homberg, M., Teklesadik, A., and Boult, V.: Analysing the cost-effectiveness of early action for food security through forecasting shortages in maize calories; a case study for Ethiopia , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11503, https://doi.org/10.5194/egusphere-egu21-11503, 2021.

Donghoon Lee, Frank Davenport, Shraddhanand Shukla, Greg Husak, and Chris Funk

In Sub-Saharan Africa, forecasting of agricultural production is becoming increasingly important for the management of the agricultural supply chain, market prediction, and food aid. More importantly, agricultural forecasts can enhance the ability of governments and humanitarian organizations to respond better to food production shocks and price spikes caused by extreme droughts. Here, we use earth observation (EO) and machine learning (ML) techniques to develop 1-6 months ahead end-of-season maize yield forecast models for several regions in Sub-Saharan Africa. We find that ML models present different aspects of forecast accuracy compared to baseline regression models. Specifically, we investigate 1) skillful EO predictors and their predictability in a given region and lead-time and 2) the benefits of using finer time resolution of EO data that can potentially capture temporal dynamics in early reproductive stages. Overall, this study provides the groundwork for an operational crop yield forecast and famine warning system. Actionable famine risk predictions can radically improve existing disaster management practices of aid organizations by providing advanced preparedness and response strategies.

How to cite: Lee, D., Davenport, F., Shukla, S., Husak, G., and Funk, C.: Maize yield forecast using earth observation data and machine learning for Sub-Saharan Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1442, https://doi.org/10.5194/egusphere-egu21-1442, 2021.

Rogerio Bonifacio, Gabriela Guimaraes Nobre, and Daniela Cuellar

To support livelihoods who rely on agricultural activities against increasing climate and food insecurity risks, the World Food Programme is implementing Forecast-based Financing (FbF) for drought management in Mozambique. FbF is an approach in which humanitarian financing and anticipatory action are automatically made available based on a certain likelihood of a drought event.

To enable the implementation of FbF projects, the World Food Programme has developed and implemented probabilistic seasonal forecasts of Standardized Precipitation Index (SPI) covering Mozambique’s rainfall season (October-April). The system produces forecast of the probability of the SPI to be less than -1, a threshold that identifies significant drought events at time scales of 2 and 3 months. These are derived from ECMWF ensemble seasonal daily precipitation forecasts, available monthly and processed from August to February to forecast drought occurrence one to six months ahead of time in four Mozambican districts.

Operational usage of probabilistic SPI forecasts requires both the derivation of a trigger (a probability value above which drought is assumed to take place) and an assessment of forecast skill. The trigger is a probability value above which drought is assumed to take place and its exceedance leads to the implementation of anticipatory actions. Forecast skill determines if the forecast system for a specific SPI time frame is usable. Both forecast skill and triggers are derived jointly through a forecast verification analysis based on a comparison between historical time series of SPI forecasts (1993-2019)  and SPI values derived from CHIRPS satellite rainfall estimates used as a reference precipitation data set.

The outcomes of this analysis are compiled into manageable tables of forecast analysis that were readily applied for decision-making process in four districts in Mozambique. In addition, a preliminary cost loss analysis for some of the Forecast-based Financing interventions against droughts and food insecurity demonstrated a potential to generate large socio-economic gains for both WFP and the beneficiaries of the anticipatory actions.

The goal of this abstract is to present WFP’s on-going and previous technical activities linked to the development of Forecast-based Financing projects for drought risk management to the broader scientific community. Whereas this system is being consolidated and still under review, next technical developments will comprise the better integration of hazard indicators with “impact levels” and risk metrics, adequate bias correction and benchmarking with other existing forecasting systems. Finally, WFP is  committed in producing evidences that can protect livelihoods and save lives through the great window of opportunity generated by actionable forecasts.


How to cite: Bonifacio, R., Guimaraes Nobre, G., and Cuellar, D.: Drought Forecasting, Thresholds and Triggers: Implementing Forecast-based Financing in Mozambique, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10434, https://doi.org/10.5194/egusphere-egu21-10434, 2021.

Marijke Panis, Aklilu Teklesadik, Mark Powell, Richard Muchena, and David Muchatiza

Historically droughts are one of the natural hazards in Zimbabwe with a significant impact on community resilience and threaten the livelihood of already vulnerable people. Agricultural activities are the primary source of income, where the dominant rain-fed agriculture is exceptionally vulnerable to climate extremes, reducing the country's agricultural productivity. The Zimbabwe Red Cross targets crop losses as the drought impact to prioritize in the drought impact-based forecasting system.

The Impact-based Forecasting project in Zimbabwe aims to reduce the impact of drought (crop losses) to the community by implementing early actions within sufficient operational lead time. Drought is a slow-onset disaster, and its impact is felt and visible at different moments  the seasonal calendar. This drought impact can be categorized into primary- and secondary impacts. Primary drought impacts are directly linked to rainfall scarcity, such as reduced crop yield and water scarcity. Secondary drought impacts are directly connected to dry conditions, such as food insecurity and epidemics. These temporal differences of impacts ask for drought triggers at various moments in the calendar, leading to a more segmented approach. The segmented approach makes it possible to design the trigger in a way that the drought indicators best linked to the operational early action at that lead time. The first phase has the longest lead time in predicting the impact of a drought using a global climatological indicator (ENSO) first to identify the probability of an El Niño/La Niña year to develop into the next growing season. Secondly, the FEWSNET Food Security Seasonal Outlook can be used as a predictor of the impact of an upcoming drought and of the population exposed to an IPC-Class 3 level. The last phase exists of monitoring biophysical drought indicators over the growing season to predict accurately the effect of a drought with the shortest lead time. The aim of phasing the trigger methodology is to activate low-cost actions when the uncertainty of the impact of a drought is relatively high. By adding more seasonal information to the trigger model over time, the predictive uncertainty reduces.

As a result, the drought trigger methodology we designed can drive the discussion and be the evidence base on the selection of early actions to reduce drought impacts. Next steps in the development of the system are to calculate the forecast skill of the biophysical indicators such as standardized precipitation index (SPI) and Vegetation Condition Index (VCI) soil moisture? linked to the identified prioritized drought impacts and to select corresponding early actions.

How to cite: Panis, M., Teklesadik, A., Powell, M., Muchena, R., and Muchatiza, D.: Overcoming the complexity of drought by phasing drought triggers; a case study of Zimbabwe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15974, https://doi.org/10.5194/egusphere-egu21-15974, 2021.

Joanna Faure Walker and Rebekah Yore

In order to be effective, warning systems need to both reach those at risk and prompt appropriate action. We study the efficacy of early warning systems in prompting residents to take appropriate action ahead of severe hazards in island countries that experience regular disasters, namely following the Great East Japan Earthquake and Tsunami in Japan, Typhoon Yolanda in The Phillippines, and Hurricane Maria in Dominica. All these events were extreme in their impact and in addition had aspects which surprised residents such as the size of the tsunami, the strom surge and the late change in intensity which provided challenges with warning. We find that multiple forms of warning are needed in order for the whole population to be reached as no one form of warning reaches everyone. The timing of the warning is important for evacuation decisions including who stays and who evacuates. It is important that the whole cycle of a warning system is considered, and that it is viewed as a process, such that we consider the scientific, communications, social and infrastructure aspects of warning systems.

How to cite: Faure Walker, J. and Yore, R.: Warning and evacuation, case studies from Japan, Philippines and Dominica, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16187, https://doi.org/10.5194/egusphere-egu21-16187, 2021.

Demi Vonk, Marc van den Homberg, Nanette Kingma, Dinand Alkema, Aklilu Teklesadik, Damien Riquet, and Maarten van Aalst

With a global paradigm shift from post-disaster response aid to anticipatory action, the question on how anticipatory action relates to long-term climate adaptation and often government-led actions towards permanent disaster prevention becomes more relevant. With rising disaster risk, a framework that decision-makers can use to select between preventive and preparedness risk reduction efforts would be most useful. A model originally developed to compare permanent interventions to forecast-based action for floods was applied to wind-induced building damage due to tropical cyclones, focusing on a case study from the Philippines. We made use of a typhoon forecasting model based on the ensemble forecast from EMCWF, and modeled the wind footprint to estimate the wind speed in the case study area. A threshold was defined, similar to how it is done in actual operations by the Philippine Red Cross. If the forecasted typhoon exceeds a pre-set threshold in terms of wind speed, action to strengthen light-weight wooden houses with a Shelter Strengthening Kit (SSK) is taken. SSKs temporarily make these houses more resistant to withstand extreme winds, thereby reducing the impacts. This short term action is compared to a scenario in which lightweight wooden houses are permanently upgraded. Results give actors in humanitarian response, anticipatory action as well as permanent disaster prevention insight into which variables affect this balance. and help policymakers to allocate their scarce budgets in a cost-effective way. The framework, although developed for the Philippines, can also be replicated in other cyclone-prone countries. 

How to cite: Vonk, D., van den Homberg, M., Kingma, N., Alkema, D., Teklesadik, A., Riquet, D., and van Aalst, M.: Balancing permanent and forecast-based action to lessen wind-induced building damage in the Philippines., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15290, https://doi.org/10.5194/egusphere-egu21-15290, 2021.

Andrea Ficchì, Hannah Cloke, Linda Speight, Douglas Mulangwa, Irene Amuron, Emmanuel Ntale, and Liz Stephens

Global flood forecasting systems are helpful in complementing local resources and in-country data to support humanitarians and trigger early action before an impactful flood occurs. Freely available global flood forecast information from the European Commission’s Global Flood Awareness System (GloFAS, a Copernicus EMS service) is being used by the Uganda Red Cross Society (URCS) alongside in-country knowledge to develop appropriate triggers for early actions for flood preparedness, within the Forecast-based Financing (FbF) initiative. To scale up the first FbF pilot to a national level, in 2020 URCS collaborated with several partners including the Red Cross Red Crescent Climate Centre (RCCC), the Uganda’s Ministry of Water and Environment, through the Directorate of Water Resources Management (DWRM), the Uganda National Meteorological Authority (UNMA), the 510 Global team and the University of Reading, through the UK-supported project Forecasts for Anticipatory Humanitarian Action (FATHUM). The new Early Action Protocol (EAP) for floods, submitted to the IFRC’s validation committee in September 2020, is now under review.

One of the aims of an EAP is to set the triggers for early action, based on forecast skill information, alongside providing a local risk analysis, and describing the early actions, operational procedures, and responsibilities. Working alongside our partners and practitioners in Uganda, we developed a methodology to tailor flood forecast skill analysis to EAP development, that could be potentially useful for humanitarians in other Countries and forecasters engaging with them. The key aim of the analysis is to identify skilful lead times and appropriate triggers for early action based on available operational forecasts, considering action parameters, such as an Action Lifetime of 30 days, and focusing on relevant flood thresholds and skill scores. We analysed the skill of probabilistic flood forecasts from the operational GloFAS (v2.1) system across Uganda against river flow observations and reanalysis data. One of the challenges was to combine operational needs with statistical robustness requirements, using relevant flood thresholds for action. Here we present the results from the analysis carried out for Uganda and the verification workflow, that we plan to make openly available to all practitioners and scientists working on the implementation of forecast-based actions.

How to cite: Ficchì, A., Cloke, H., Speight, L., Mulangwa, D., Amuron, I., Ntale, E., and Stephens, L.: Flood forecast skill for Early Action: Results and Learnings from the development of the Early-Action Protocol for Floods in Uganda, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16169, https://doi.org/10.5194/egusphere-egu21-16169, 2021.

Sazzad Hossain, Hannah Cloke, Andrea Ficchì, Christel Prudhomme, Arifuzzaman Bhuyan, and Elisabeth Stephens

Flood is a frequent natural hazard in the Brahmaputra basin in Bangladesh during the South Asian summer monsoon between June to September. When will flooding start during monsoon and how long it will last are two important questions that forecasters need to answer. Predicting flood timing and duration with a sufficient lead-time is challenging for forecasters due to strong intraseasonal variation of floods within a monsoon.

The GloFAS forecasting system is run by ECMWF as part of the Copernicus Emergency Management Service and provides operational extended-range ensemble flood forecast with 30 days lead-time for the major river basins in the world. In this study, we evaluated GloFAS reforecasts for the Brahmaputra basin in Bangladesh for the period 1997–2019 at different lead-times against observed stream gauge and ECMWF ERA5 reanalysis river discharge data. We used various probabilistic forecast verification metrics, such as Relative Operating Characteristic (ROC), False Alarm Ratio (FAR), and Probability of Detection (POD), to study how forecast skill varies over different lead-times. We also assessed the skilful lead-times of the GloFAS forecast to predict flood timing and duration during the monsoon. These scores were calculated considering relevant flood threshold levels and action-based parameters, such as Action Lifetime, based on user needs in Bangladesh. The GloFAS forecast case study for the recent 2020 monsoon floods in the Brahmaputra basin shows that the onset of flood events was successfully predicted with a lead-time of 15 days. These forecasts were disseminated among the different stakeholders, including humanitarian agencies, flood and disaster management organisations, to inform forecast-based actions, such as evacuation of vulnerable people to safer places ahead of flood events. Our study demonstrates that GloFAS ability to predict monsoon floods in terms of timing and duration can improve national flood forecasting capabilities providing sufficient lead-time for early actions in Bangladesh. The study will help forecasters as well as users to understand forecast skill and associated uncertainty in probabilistic forecasts to predict flood events in Bangladesh.




How to cite: Hossain, S., Cloke, H., Ficchì, A., Prudhomme, C., Bhuyan, A., and Stephens, E.: Assessment of GloFAS ensemble flood forecast for the Brahmaputra basin: skilful lead-times to predict monsoon floods for early action in Bangladesh, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13746, https://doi.org/10.5194/egusphere-egu21-13746, 2021.

Jonathan Lala, Juan Bazo, Vaibhav Anand, and Paul Block

Natural disaster management has recently seen a major innovation through the advent of standardized forecast-based action and financing protocols. Given a forecast with adequate skill and lead time, relief actions can be taken before, rather than after, a disaster, saving lives and property while also transferring some ex-post risk to ex-ante risk for the relief agency. Multi-stage actions, in which forecasts with longer leads allow for preparation while short-term forecasts trigger direct actions, may be particularly effective at reducing risk. Multi-stage protocols, however, have not been explicitly optimized, either through trigger mechanisms or forecast tailoring. This study considers a multi-stage early action protocol developed by the Peruvian Red Cross for El Niño-induced extreme rainfall in coastal Peru. A sensitivity analysis of trigger thresholds, forecast methods, and levels of risk aversion is conducted to recommend optimal actions. Results demonstrate the relative importance of benefit-cost ratios at different lead times; forecast technology and risk aversion play a lesser but still valuable role. Moreover, the optimization framework can be utilized without post-disaster monitoring and evaluation, enabling the proliferation of effective plans in other disaster-prone regions.

How to cite: Lala, J., Bazo, J., Anand, V., and Block, P.: Optimizing forecast-based actions for extreme rainfall in Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-521, https://doi.org/10.5194/egusphere-egu21-521, 2021.

Tim Busker, Toon Haer, Jeroen Aerts, Hans de Moel, Bart van den Hurk, and Kira Myers

Research shows that climate change will increase the intensity and frequency of extreme summer precipitation events as well as heatwaves, over the coming decades (IPCC, 2014; Russo et al., 2015). Moreover, the impact of heat waves will likely increase in cities due to the urban heat island (UHI) effect (Li & Bou-Zeid, 2013). Green infrastructure (e.g. parks, green roofs, etc.) is generally seen as an effective adaptation measure to address these challenges. The city of Amsterdam has started a project (RESILIO, https://resilio.amsterdam/en/smart-blue-green-roofs) to investigate a new innovation in this field: smart blue-green roofs. These roofs have the advantage over green roofs in that they have an extra water retention layer underneath the green layer, which can be used to buffer peak rainfall or as a capillary irrigation system for the plant layer in hot and dry summer days. The smart valve on the roof can be opened when extreme precipitation is predicted to capture extreme rainfall, but it is yet unknown if this forecast-based drainage provides added value to optimize the operation of the valve.

Therefore, this study evaluates the performance of European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble precipitation forecasts to trigger drainage from blue-green roofs. A conceptual hydrological model of a blue-green roof in Amsterdam is set up to simulate its operation for the last 5 years. Three drainage strategies can be triggered according to different probabilities of precipitation (30th, 60th and 90th percentile) based on ECMWF data. Each strategy is evaluated on how it leads to (1) minimize the overflow during peak rainfall into the city drainage system, and (2) to maintain high water levels during hot summer days to boost evaporative cooling. Preliminary results show that some early drainage strategies result in capturing 50-100% of rainfall (>10mm/hr), while enough water is available on most hot summer days (T>25℃) to ensure atmospheric cooling through plant transpiration. This implies that relatively low-resolution (18km) precipitation forecasts from ECMWF are valuable for anticipatory water management on a very local scale. These results also show the high potential of blue-green roofs for urban climate adaptation, and the need for anticipatory management of these nature-based solutions. The next research steps will include a city-scale roof suitability analysis that will reveal the value of this solution when implemented at most flat roofs in the city of Amsterdam.

IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

Li, D., & Bou-Zeid, E. (2013). Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts. Journal of Applied Meteorology and Climatology. https://doi.org/10.1175/JAMC-D-13-02.1

Russo, S., Sillmann, J., & Fischer, E. M. (2015). Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environmental Research Letters. https://doi.org/10.1088/1748-9326/10/12/124003

How to cite: Busker, T., Haer, T., Aerts, J., de Moel, H., van den Hurk, B., and Myers, K.: Forecast-based operation of smart blue-green roofs to reduce the impacts of extreme weather in cities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12768, https://doi.org/10.5194/egusphere-egu21-12768, 2021.

Soufiane el Khinifri, Marc van den Homberg, Tessa Kramer, Joost Beckers, Jaap Schellekens, Albert Kettner, Abdoul Aziz Mounkaila Issaka, Issoufou Maigary, Mamadou Adama Sarr, and Johannes Reiche
Water supports life, however it does come with hazards. Floods area amongst the most impactful environmental disasters. Accurate flood forecasting and early warning are critical for disaster risk management. Detecting and forecasting floods at an early stage is certainly relevant for Mali, hence crucial in order to prevent a hazard from turning into a disaster. Remotely sensed river monitoring can be an effective, systematic and time-efficient technique to detect and forecast extreme floods. Conventional flood forecasting systems require extensive data inputs and software to model floods. Moreover, most models rely on discharge data, which is not always available and is less accurate in a overbank flow situations. There is a need for an alternative method which detects riverine inundation, while making use of the available state-of-the-art.
This research investigates the use of passive microwave remote sensing with different spatial resolutions for the detection and forecasting of flooding. Brightness temperatures from two different downscaled spatial resolutions  (1 x 1 km and 10 x 10 km) are extracted from passive microwave remote sensing sensors and are converted into discharge estimators: a dry CM ratio and a wet CMc ratio. Surface water has a low emission, thus let the CM ratio increase as the surface water percentage in the pixel increases. Sharp increases are observed for over-bank flow conditions.

Overall, we compared the passive microwave remote sensing model results of the different spatial resolutions to the results of a conventional global runoff model GloFAS. The passive microwave remote sensing model does not require extensive input data when used as an Early Warning System (EWS), as many smaller-scale EWS do, we suggest that when improved, the passive microwave remote sensing method is implemented as part of an integrative EWS solution, including a passive microwave remote sensing model and various other models. This would allow for early warnings in data-scarce regions and at a variety of lead times. In order for this to be effective, we suggest that more research be done on correctly setting the trigger threshold, and into the potential spatial interpretation of CMc.

How to cite: el Khinifri, S., van den Homberg, M., Kramer, T., Beckers, J., Schellekens, J., Kettner, A., Mounkaila Issaka, A. A., Maigary, I., Sarr, M. A., and Reiche, J.: Flood monitoring using passive microwave remote sensing in the Senegal River, Western Mali, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15223, https://doi.org/10.5194/egusphere-egu21-15223, 2021.

Inez Gortzak, Marc van den Homberg, Jacopo Margutti, Christopher Beddow, and Maarten van Aalst

To accurately identify the most vulnerable areas to floods, physical (e.g., building material) and social (e.g., education, health, income of households) housing stock information is required. However, in developing countries, this information is often unreliable, unavailable or inaccessible, and manual data collection is time-consuming. This can lead to difficulties for humanitarians or policymakers in implementing appropriate disaster risk reduction and response interventions. Therefore, there is a need for the development of alternative approaches to data collection and analysis. An alternative approach to on-site vulnerability assessment is to extract physical vulnerability characteristics, such as land use type or rooftop material, from satellite or Unmanned Aerial Vehicle (UAV) imagery. However, other social or physical vulnerability information on the household level can often not be extracted from only the remote sensing data. This research develops an approach for integrating multiple data sources into a Geographic Information System to improve the completeness of data on different vulnerability indicators. This approach is applied on the housing stock of the Karonga district in Malawi. An Object-Based Image Analysis on UAV imagery is combined with a machine learning analysis of Mapillary data to enable remote identification of both rooftop ànd wall material. Depth-damage curves were created to describe the impact on the housing stock for different categories of physical vulnerability (such as building material) and levels of inundation. Moreover, local survey data is used for the creation of a social vulnerability index. Combined, the datasets represent the spatial distribution of housing stock vulnerability for multiple flood scenarios. This approach is useful in situations where proactive risk analyses must be carried out or where local-scale interventions, such as building strengthening- or flood awareness projects, have to be implemented. Finally, we give recommendations for scaling the methodology to areas where only lower resolution data is available.

How to cite: Gortzak, I., van den Homberg, M., Margutti, J., Beddow, C., and van Aalst, M.: Characterizing housing stock vulnerability to floods by combining UAV, Mapillary and survey data – A case study for Karonga, Malawi, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12810, https://doi.org/10.5194/egusphere-egu21-12810, 2021.